Speed up matrix operations with TensorFlow’s experimental API

If you have used Python for any data processing, you have most likely used NumPy (short for Numerical Python). It provides a rich arsenal of complex data types and efficient matrix manipulation functions. Its C-accelerated implementation of vectorisable functions has earned it its reputation for processing n-dimensional array at lightning speed. But can we go faster than that?

NumPy’s C-accelerated implementation of vectorisable functions enables us to efficiently process large multi-dimensional arrays

In comes, TensorFlow’s take on NumPy API.

Thanks to TensorFlow’s GPU acceleration, we can now run NumPy **situationally** even faster than we can already — faster than lightning. What’s also better is that Tensorflow’s NumPy is also intra-operatable with TensorFlow’s APIs and the original NumPy API. That means we can pass TensorFlow’s `ndarray`

to other Tensorflow functions under `tf.linalg`

, `tf.data`

, `tf.keras`

, `tf.signal`

, etc. We can even further distribute our workload with `tf.distribute`

.

TensorFlow’s NumPy API attempts to speed up NumPy by accelerating it with GPUs alongside with the benefit of being able to access other TensorFlow API

Note: This is still an experimental API from Tensorflow so do expect (significant) changes in the (near) future.

In this blog, we are going to take a look exactly how much faster, and under what scenario should we be using Numpy or TensorFlow. We will look into 4 very common usecases for Numpy that we as data scientists, software engineer or machine learning practicioner will be using on a daily basis:

- Sorting
- Linear Algebra
- Mathematical Function
- Fast Fourier Transform

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